
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 							lowerCAmelCase__   ( _A						):
						UpperCamelCase_				:			Dict						=		["image_processor", "tokenizer"]
						UpperCamelCase_				:			int						=		"ViltImageProcessor"
						UpperCamelCase_				:			List[Any]						=		("BertTokenizer", "BertTokenizerFast")

						def __init__(      self							,				a=None							,				a=None							,				**a					)       ->   Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase         =						None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""							,				a_							,				)
										_UpperCamelCase         =						kwargs.pop("""feature_extractor"""					)

								_UpperCamelCase         =						image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""					)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""					)

								super().__init__(a_							,				a_					)
								_UpperCamelCase         =						self.image_processor

						def __call__(      self							,				a							,				a = None							,				a = True							,				a = False							,				a = None							,				a = None							,				a = 0							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = False							,				a = False							,				a = True							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.tokenizer(
								    text=a_							,				add_special_tokens=a_							,				padding=a_							,				truncation=a_							,				max_length=a_							,				stride=a_							,				pad_to_multiple_of=a_							,				return_token_type_ids=a_							,				return_attention_mask=a_							,				return_overflowing_tokens=a_							,				return_special_tokens_mask=a_							,				return_offsets_mapping=a_							,				return_length=a_							,				verbose=a_							,				return_tensors=a_							,				**a_							,				)
								# add pixel_values + pixel_mask
								_UpperCamelCase         =						self.image_processor(a_							,				return_tensors=a_					)
								encoding.update(a_					)

								return encoding

						def 			A_					(      self							,				*a							,				**a					)       ->   Dict:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a_							,				**a_					)

						def 			A_					(      self							,				*a							,				**a					)       ->   List[str]:


								'''simple docstring'''

								return self.tokenizer.decode(*a_							,				**a_					)

						@property
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.tokenizer.model_input_names
								_UpperCamelCase         =						self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)

						@property
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,				a_							,				)
								return self.image_processor_class







						@property
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,				a_							,				)
								return self.image_processor










'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



